Abnormality diagnosing method and abnormality diagnosing system

ABSTRACT

An abnormality diagnosing method includes a model generation step of generating a simulation model of a monitoring target, an operation start step of starting an operation of the monitoring target, a measurement step of measuring an internal state quantity in the operating state of the monitoring target and extracting a measured value, a prediction step of inputting into the simulation model same control input value used in the operating state of the monitoring target and calculating a predicted value of the internal state quantity of the monitoring target, a Mahalanobis distance calculation step of calculating a Mahalanobis distance from a difference between the measured value and the predicted value, and an abnormality diagnosis step of diagnosing whether the operating state of the monitoring target is abnormal based on the Mahalanobis distance.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of InternationalApplication No. PCT/JP2016/054579, filed on Feb. 17, 2016, which claimspriority to Japanese Patent Application No. 2015-029249, filed on Feb.18, 2015, the entire contents of which are incorporated by referenceherein.

BACKGROUND 1. Technical Field

The present disclosure relates to an abnormality diagnosing method andan abnormality diagnosing system. Particularly, the present disclosurerelates to an abnormality diagnosing method and an abnormalitydiagnosing system suitable for detecting an abnormality in a non-steadystate that changes dynamically.

2. Description of the Related Art

In the field of various plants such as a gas turbine power plant, anuclear power plant, a thermal power plant, and in the field of aninternal-combustion engine such as a jet engine, an abnormalitydiagnosis of the plant or the engine is performed by monitoring anoperating state (including a test operation) thereof to realize a stableoperation and output.

For example, Japanese Patent Application Laid-Open No. 2011-090382(Patent Literature 1) discloses a monitoring system in which a series ofprocesses from monitoring of an indication of an abnormality of amonitoring target to a troubleshooting can be automated. This monitoringsystem includes a monitoring unit that acquires predetermined monitoringtarget data from the monitoring target, calculates a Mahalanobisdistance thereof, and detects an abnormality in the monitoring target, adata processing unit that generates a predetermined input signal byextracting an abnormal signal indicating an indication of an abnormalityand a related signal that is related monitoring target data, and amalfunction diagnosing unit that performs the troubleshooting withrespect to the monitoring target based on the input signal.

Japanese Patent Application Laid-Open No. 2014-035282 (Patent Literature2) discloses an abnormality diagnosing apparatus that diagnoses anabnormality of a plant by comparing values of a plurality of variablesinput newly from the plant with a predetermined unit space. Thisabnormality diagnosing apparatus includes an accumulated data storingunit that stores therein accumulated data including a value of each ofthe variables input in the past, a deciding unit that, for each of thevariables, extracts a maximum value and a minimum value within apredetermined period of the accumulated data and decides a central valueof these as a median value, a first calculating unit that calculates adifference between the value newly input for each of the variables andthe median value, a second calculating unit that calculates aMahalanobis distance by using the calculated difference for each of thevariables and data of the predetermined unit space, and a determiningunit that diagnoses an abnormality by determining whether theMahalanobis distance is within a threshold range set beforehand.

SUMMARY

A monitoring target such as a plant or an internal-combustion enginegenerally has a steady state that is a stable operating state and anon-steady state that is a transient unstable operating state before themonitoring target reaches the steady state. In the non-steady state, thesame monitoring target behaves differently depending on environmentalconditions, operating conditions, and the like at a given time, andalmost never shows the same dynamic change.

In the monitoring system disclosed in Patent Literature 1, bycalculating the Mahalanobis distance of the monitoring target data, theinput signal used in the troubleshooting is generated from the abnormalsignal indicating the indication of the abnormality and the relatedsignal. However, to determine whether there is the abnormality or theindication of the abnormality after calculating the Mahalanobis distanceof the monitoring target data, it is necessary to prepare reference databeforehand. In case of the steady state, because the operating state andthe outputting state are stable, it is possible to prepare the referencedata. However, in case of the non-steady state that changes dynamically,the reference data cannot be generated from only the monitoring targetdata, so that the abnormality diagnosis cannot be performed.

Also in the abnormality diagnosing apparatus disclosed in PatentLiterature 2, because the Mahalanobis distance is calculated by usingthe accumulated data of the past, like Patent Literature 1, although theabnormality diagnosis can be performed for the steady state bycomparison thereof with the past data, the abnormality diagnosis cannotbe performed for the non-steady state.

This disclosure has been made in view of the above discussion. Oneobject of the present disclosure is to provide an abnormality diagnosingmethod and an abnormality diagnosing system that can perform theabnormality diagnosis not only in the steady state of the monitoringtarget but also in the non-steady state.

A first aspect of the present disclosure is an abnormality diagnosingmethod of diagnosing an abnormality of a monitoring target having anoperating state that includes a non-steady state, the method including:generating a simulation model of the monitoring target; measuring aninternal state quantity in the operating state of the monitoring targetand extracting a measured value; inputting into the simulation modelsame control input value used in the operating state of the monitoringtarget and calculating a predicted value of the internal state quantityof the monitoring target; calculating a Mahalanobis distance from adifference between the measured value and the predicted value; anddiagnosing whether the operating state of the monitoring target isabnormal based on the Mahalanobis distance.

The method may include calculating an error vector that includes thedifference and an integral value of the difference as componentsthereof. Moreover, the calculating of the predicted value may be madebased on a measured value that was measured immediate previously in atime series.

A second aspect of the present disclosure is an abnormality diagnosingsystem for diagnosing an abnormality of a monitoring target having anoperating state that includes a non-steady state, the system including:a simulation model configured to simulate the monitoring target; ameasuring unit configured to measure an internal state quantity in theoperating state of the monitoring target; a diagnosing device configuredto calculate a Mahalanobis distance from a difference between apredicted value calculated by the simulation model and a measured valueextracted by the measuring unit and diagnoses whether the operatingstate of the monitoring target is abnormal based on the Mahalanobisdistance; and a controlling unit configured to transmit same controlinput value to at least the monitoring target and the simulation model.

The diagnosing device may calculate the Mahalanobis distance based on anerror vector that includes the difference and an integral value of thedifference as components thereof. Moreover, the simulation model maycalculate the predicted value based on a measured value that wasmeasured immediate previously in a time series. The monitoring targetis, for example, an engine for reusable spacecraft.

In the abnormality diagnosing method and the abnormality diagnosingsystem according to the present disclosure, a simulation model thatsimulates an internal state of a monitoring target is generated, andwhether the monitoring target is abnormal is diagnosed by using adifference between a measured value obtained from the monitoring targetand a predicted value calculated by the simulation model. Accordingly,the predicted value that suits with the environmental conditions and/orthe operating conditions at the time the abnormality diagnosis is madecan be calculated by the simulation model, and, because the differencehas been used, the measured value obtained from the monitoring targetcan be replaced with a variation value of a normal value. Accordingly,even if the operating state of the monitoring target is the non-steadystate, the dynamic change thereof can be followed and an action can betaken, and the abnormality diagnosis of the monitoring target can beperformed not only in the steady state but also in the non-steady state.Moreover, by using the Mahalanobis distance in the abnormalitydiagnosis, the abnormality diagnosis can be made simple and fast.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overall structural schematic diagram of an abnormalitydiagnosing system according to the present disclosure.

FIG. 2 is a flowchart of an abnormality diagnosing method according tothe present disclosure.

FIGS. 3A and 3B are drawings for explaining a Mahalanobis distancecalculation step, where FIG. 3A shows an error vector and FIG. 3B showsan example of a calculation method of a predicted value.

FIGS. 4A and 4B are drawings for explaining an abnormality diagnosisstep, where FIG. 4A is a conceptual diagram of a Mahalanobis distanceand FIG. 4B is a conceptual diagram of an abnormality diagnosis.

FIGS. 5A to 5C are explanatory drawings to verify the efficacy when thepresent disclosure is applied to an engine for reusable spacecraft,where a result of the abnormality diagnosis based on a control inputvalue is shown in FIG. 5A, the same based on simulated data of ameasured value is shown in FIG. 5B, and the same based on theMahalanobis distance is shown in FIG. 5C.

DESCRIPTION OF THE EMBODIMENTS

Exemplary embodiments according to the present disclosure are explainedbelow by using the accompanying drawings. FIG. 1 is an overallstructural schematic diagram of an abnormality diagnosing systemaccording to the present disclosure. FIG. 2 is a flowchart of anabnormality diagnosing method according to the present disclosure. FIGS.3A and 3B are drawings for explaining a Mahalanobis distance calculationstep, where FIG. 3A shows an error vector and FIG. 3B shows an exampleof a calculation method of a predicted value. FIGS. 4A and 4B aredrawings for explaining an abnormality diagnosis step, where FIG. 4A isa conceptual diagram of a Mahalanobis distance and FIG. 4B is aconceptual diagram of an abnormality diagnosis.

An abnormality diagnosing system 1 according to one embodiment of thepresent disclosure is, as shown in FIG. 1, an abnormality diagnosingsystem for diagnosing a monitoring target 2 having an operating statethat includes a non-steady state. The abnormality diagnosing system 1includes a simulation model 3 that simulates the monitoring target 2, ameasuring unit 4 that measures a predetermined internal state quantityin the operating state of the monitoring target 2, a diagnosing device 5that calculates a Mahalanobis distance MD from a difference (x̂−x)between a predicted value x calculated by the simulation model 3 and ameasured value x̂ (̂ (circumflex or hat) on x. Same holds true in thebelow explanation.) extracted from the measuring unit 4, and thatdiagnoses whether the operating state of the monitoring target 2 isabnormal based on the Mahalanobis distance MD, and a controlling unit 6that transmits the same control input value u to both the monitoringtarget 2 and the simulation model 3.

The monitoring target 2 is, for example, an engine for reusablespacecraft. However, the monitoring target 2 is not limited to theengine for reusable spacecraft and can be any other internal-combustionengine such as a jet engine, various plants such as a gas turbine powerplant, a nuclear power plant, a thermal power plant, a chemical plant,and the like. Particularly, it is desirable that the monitoring target 2has a steady state that is a stable operating state and a non-steadystate that is a transient unstable operating state before reaching thesteady state.

The simulation model 3 is a model that allows an estimation of theinternal state quantity of the monitoring target 2. The simulation model3 is generated, for example, by applying a numerical simulationtechnique. In generating the simulation model, a recurrence relationexpression (ARMA) can be used in consideration of a real-time process.When the monitoring target 2 is, for example, the engine for reusablespacecraft, as the internal state quantity, for example, a combustionpressure Pc, a regenerative cooling outlet temperature Tjmf, a fuel pumprotation frequency Nf, an oxidant pump rotation frequency No, a fuelpump outlet pressure Pdf, an oxidant pump outlet pressure Pdo, and thelike, can be selected. Accordingly, the simulation model that allowscalculation of these internal state quantities is generated. Thesimulation model 3 can be one simulation model that simulates the entiremonitoring target 2 or can be constituted by a plurality of simulationmodels each of which calculates a different internal state quantity.

The measuring unit 4 is installed in the monitoring target 2. Themeasuring unit 4 is, for example, a sensor that measures one or more ofthe internal state quantities such as the combustion pressure Pc, theregenerative cooling outlet temperature Tjmf, the fuel pump rotationfrequency Nf, the oxidant pump rotation frequency No, the fuel pumpoutlet pressure Pdf, and the oxidant pump outlet pressure Pdo. Themeasuring unit 4 is, for example, a pressure gauge, a thermometer, arotary encoder, and the like. However, the measuring unit 4 is notlimited to these devices, and can be selected appropriately based on thetype of the monitoring target 2 and/or the internal state quantity to bemeasured.

The controlling unit 6 is a device that transmits to the monitoringtarget 2 the control input value u necessary to operate the monitoringtarget 2. The operating state of the monitoring target 2 can be anactual operation or can be a test operation. Moreover, the controllingunit 6 transmits also to the simulation model 3 the control input valueu necessary to operate the monitoring target 2. The simulation model 3calculates an internal state quantity based on this control input valueu, and also calculates a predicted value x for each of the internalstate quantities. It is allowable to measure an output value y of themonitoring target 2 that is operated by using the control input value u,and extract the output value y to the outside.

The diagnosing device 5 is a device that receives data of the measuredvalue x̂ measured by the measuring unit 4 and data of the predicted valuex calculated by the simulation model 3, and performs an abnormalitydiagnosis of the monitoring target 2 by using the received data. Thediagnosing device 5 performs a process based on, for example, theflowchart shown in FIG. 2. A diagnosis result and diagnosis data can beoutput from the diagnosing device 5 to the outside by various methodssuch as displaying on a monitor, printing on a paper, outputting in theform of data.

As shown in FIG. 2, the flowchart has the following steps: a modelgeneration step (Step 1) of generating the simulation model 3 of themonitoring target 2, an operation start step (Step 2) of starting theoperation of the monitoring target 2, a measurement step (Step 3) ofmeasuring the internal state quantity in the operating state of themonitoring target 2 and extracting the measured value x̂, a predictionstep (Step 4) of inputting into the simulation model 3 the same controlinput value u used in the operating state of the monitoring target 2 andcalculating the predicted value x of the internal state quantity of themonitoring target 2, a Mahalanobis distance calculation step (Step 5) ofcalculating the Mahalanobis distance MD from the difference (x̂−x)between the measured value x̂ and the predicted value x, and anabnormality diagnosis step (Step 6) of diagnosing whether the operatingstate of the monitoring target 2 is abnormal based on the Mahalanobisdistance MD.

The diagnosing device 5 performs the Mahalanobis distance calculationstep (Step 5) and the abnormality diagnosis step (Step 6). In theabnormality diagnosing method according to the present embodiment,whether the obtained data (measured value x̂) is abnormal is diagnosedbased on multivariable analysis that uses the Mahalanobis distance. Acorrelation among a plurality of variables can be processed at one timeby using the Mahalanobis distance. That is, because it is not necessaryto separately perform the diagnosis per variable to decide whether thevariable is abnormal, the abnormality diagnosis can be made simple andfast.

The Mahalanobis distance calculation step (Step 5), as shown in FIG. 2,can include a difference calculation step (Step 51) of calculating thedifference (x̂−x) between the measured value x̂ and the predicted value x,an error vector calculation step (Step 52) of calculating an errorvector ε having the difference (x̂−x) and an integral value Σε of anerror as components thereof, and a Mahalanobis distance computation step(Step 53) of calculating the Mahalanobis distance MD based on the errorvector ε.

The error vector ε can be expressed in the manner shown in FIG. 3A. Whenan error vector that changes with time is calculated continuously, theintegral value Σε, which constitutes a component of the error vector ε,can be calculated as a so-called integral value. To calculate the errorvector ε per a certain period of time (span), the integral value Σε canbe calculated as a grand total of the difference (x̂−x). In this manner,a cumulative error evaluation sensitivity in the same direction can beprevented from becoming weak by use of the integral value Σε of theerror (difference).

When the combustion pressure Pc, the regenerative cooling outlettemperature Tjmf, the fuel pump rotation frequency Nf, the oxidant pumprotation frequency No, the fuel pump outlet pressure Pdf, and theoxidant pump outlet pressure Pdo are selected as the internal statequantity, for example, the error vector ε can be written as a matrix of(ΔPc, ΔTjmf, ΔNf, ΔNo, ΔPdf, ΔPdo, ΣΔPc, ΣΔTjmf, ΣΔNf, ΣΔNo, ΣΔPdf,ΣΔPdo) as shown in FIG. 3A. In this example, because the error vector εincludes 12 variables, the error vector ε is contained in a vector spaceR¹² formed by these variables.

The prediction step (Step 4) includes an inputting step (Step 41) ofinputting into the simulation model 3 the same control input value u asthe operation of the monitoring target 2, and a predicted valuecalculation step (Step 42) of calculating the predicted value x of theinternal state quantity based on the control input value u. At thepredicted value calculation step Step 42 (prediction step (Step 4)), asshown in FIG. 3B, it is allowable to calculate a predicted value xnbased on a measured value x_(n−1)̂ that was measured immediatepreviously in the time series (i.e. a last value previously measured inthe time series). That is, the predicted value xn is calculated based onthe measured value x_(n−1)̂, and a predicted value x_(n+1) is calculatedbased on a measured value x_(n)̂. With this method, the accumulation ofthe error can be inhibited, the accuracy of predicted value x_(n) can beimproved, and therefore, the accuracy of the abnormality diagnosis canbe improved.

At the Mahalanobis distance computation step (Step 53), to calculate theMahalanobis distance MD from the error vector ε, at first, the errorvector ε is standardized by using Expression 1 to convert the errorvector into a state so that the error vector ε does not depend on aphysical quantity unit. To standardize the error vector ε, an entireaverage value vector during the operation period

ε  [Equation 1]

and a deviation

σε  [Equation 2]

are used.

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack & \; \\{ɛ_{n}^{''} = \left( {\frac{ɛ_{n\; 1} - \overset{\_}{ɛ_{n\; 1}}}{{\sigma ɛ}_{n\; 1}},\frac{ɛ_{n\; 2} - \overset{\_}{ɛ_{n\; 2}}}{{\sigma ɛ}_{n\; 2}},\ldots \mspace{11mu},\frac{ɛ_{n\; 12} - \overset{\_}{ɛ_{n\; 12}}}{{\sigma ɛ}_{n\; 12}}} \right)} & \left( {{Expression}\mspace{14mu} 1} \right)\end{matrix}$

where

[Equation  4]${\overset{\_}{ɛ_{n\; 1}} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}ɛ_{n}}}},{{\sigma ɛ}_{n} = \sqrt{\frac{{\Sigma \left( {ɛ_{n} - \overset{\_}{ɛ_{n}}} \right)}^{2}}{N}}}$

The error vector εn″ standardized based on Expression 1 is expressed asεn and used in the subsequent calculation.

Then, the Mahalanobis distance MD is calculated by using Expression 2.Here, ε^(T) indicates a transposed matrix of the error vector ε, anddim(ε) indicates a dimension of the error vector ε. Moreover, acovariance matrix can be derived, for example, from the accumulated dataof the past that is diagnosed as being normal.

[Equation 5]

MD_(n)=√{square root over (ε_(n)

⁻¹ε_(n) ^(T)/dim(ε))}  (Expression 2)

where

  [Equation 6]

is the covariance matrix.

By calculating the Mahalanobis distance MD and connecting equidistantpoints, for example, a correlation among the internal state quantitiesshown in FIG. 4A can be calculated. The error becomes large as one goesaway from a center of a substantially elliptical region shown in thisdrawing. Therefore, it can be diagnosed that there is an abnormalitywhen the value is outside of this region. The correlation shown in FIG.4A represents a correlation between only two variables, i.e., internalstate quantities D1 and D2, to promote an intuitive understanding. Fromthis correlation, it can be understood that a permissible value of theerror is large along the major axis of this substantially ellipticalregion, and a permissible value of the error is small along the minoraxis of this substantially elliptical region. Although not shown in thisdrawing, a 12-dimensional correlation is obtained when the 12 variablesare used as mentioned above.

At the abnormality diagnosis step Step 6, for example, as shown in FIG.4B, the Mahalanobis distance MD is calculated, with respect to the error(difference) that changes over time, each time the diagnosis isperformed. Moreover, a determination is made as to whether the error(difference) is within the Mahalanobis distance MD each time theMahalanobis distance MD is calculated. For example, a Mahalanobisdistance MD1 at time t1, a Mahalanobis distance MD2 at time t2, aMahalanobis distance MD3 at time t3, a Mahalanobis distance MD4 at timet4, and a Mahalanobis distance MD5 at time t5 change from time to timebased on the environmental conditions, the operating conditions, and thelike at a given time. The graph of FIG. 4B is shown to promote anintuitive understanding of the abnormality diagnosing method accordingto the present embodiment.

In the abnormality diagnosing method and the abnormality diagnosingsystem 1 according to the present embodiment, the simulation model 3that simulates the internal state of the monitoring target 2 isgenerated, and whether the monitoring target 2 is abnormal is diagnosedby using the difference (x̂−x) between the measured value x̂ obtained bythe monitoring target 2 and the predicted value x calculated by thesimulation model 3. Accordingly, the predicted value x that suits withthe environmental conditions and/or the operating conditions at the timethe abnormality diagnosis is made can be calculated by the simulationmodel 3. Moreover, because the difference has been used, the measuredvalue x̂ obtained by the monitoring target 2 can be replaced with avariation value of a normal value. Accordingly, even if the operatingstate of the monitoring target 2 is the non-steady state, the dynamicchange thereof can be followed and an action can be taken, and theabnormality diagnosis of the monitoring target 2 can be performed notonly in the steady state but also in the non-steady state.

FIG. 5A to FIG. 5C are explanatory drawings to verify the efficacy whenthe present disclosure is applied to the engine for reusable spacecraft,and a result of the abnormality diagnosis based on a control input valueis shown in FIG. 5A, the same based on simulated data of the measuredvalue is shown in FIG. 5B, and the same based on the Mahalanobisdistance is shown in FIG. 5C. In FIG. 5A and FIG. 5B, a thrust value isshown by a continuous line, a fuel value is shown by a dotted line, anoxidant value is shown by an alternate long and short dash line, and acombustion pressure value is shown by a two-dot chain line. In FIG. 5A,a portion of the thrust curve that protrudes up (substantiallytrapezoidal portion) simulates the non-steady state.

The amount of the fuel and the oxidant are controlled to obtain thethrust shown in FIG. 5A. Here, to verify the efficacy of the abnormalitydiagnosis performed by using the Mahalanobis distance MD, as shown inFIG. 5B, by setting an offset value (portion a in the drawing) withrespect to the normal measured value, simulated data of the measuredvalue that intentionally includes an abnormal value has been generated.When the Mahalanobis distance calculation step Step 5 is performed byusing the simulated data of the measured value and the predicted valuecalculated by the simulation model 3, a result shown in FIG. 5C isobtained.

In FIG. 5C, a continuous line shows a change of the Mahalanobis distanceMD over time, and black dots show points in time at which diagnosed asbeing abnormal. It can be understood from this verification result thatthe Mahalanobis distances MD of the parts corresponding to the offsetportions in which the abnormal values were intentionally set have beendiagnosed as being abnormal. Accordingly, it can be confirmed that theabnormality diagnosing method and the abnormality diagnosing system 1according to the present embodiment are able to perform the abnormalitydiagnosis when the operating state includes the non-steady state.

The present disclosure is not limited to the above embodiments, and itcan be implemented by making various changes in a range that do notdeviate from the gist of the present disclosure.

What is claimed is:
 1. An abnormality diagnosing method of diagnosing anabnormality of a monitoring target having an operating state thatincludes a non-steady state, the abnormality diagnosing methodcomprising: generating a simulation model of the monitoring target;measuring an internal state quantity in the operating state of themonitoring target and extracting a measured value; inputting into thesimulation model same control input value used in the operating state ofthe monitoring target and calculating a predicted value of the internalstate quantity of the monitoring target; calculating a Mahalanobisdistance from a difference between the measured value and the predictedvalue; and diagnosing whether the operating state of the monitoringtarget is abnormal based on the Mahalanobis distance.
 2. The abnormalitydiagnosing method according to claim 1, further comprising calculatingan error vector that includes the difference and an integral value ofthe difference as components thereof.
 3. The abnormality diagnosingmethod according to claim 2, wherein the calculating of the predictedvalue is made based on a measured value that was measured immediatepreviously in a time series.
 4. An abnormality diagnosing system fordiagnosing an abnormality of a monitoring target having an operatingstate that includes a non-steady state, the abnormality diagnosingsystem comprising: a simulation model that simulates the monitoringtarget; a measuring unit configured to measure an internal statequantity in the operating state of the monitoring target; a diagnosingdevice that calculates a Mahalanobis distance from a difference betweena predicted value calculated by the simulation model and a measuredvalue extracted by the measuring unit and diagnoses whether theoperating state of the monitoring target is abnormal based on theMahalanobis distance; and a controlling unit configured to transmit samecontrol input value to at least the monitoring target and the simulationmodel.
 5. The abnormality diagnosing system according to claim 4,wherein the diagnosing device calculates the Mahalanobis distance basedon an error vector that includes the difference and an integral value ofthe difference as components thereof.
 6. The abnormality diagnosingsystem according to claim 5, wherein the simulation model calculates thepredicted value based on a measured value that was measured immediatepreviously in a time series.
 7. The abnormality diagnosing systemaccording to claim 4, wherein the monitoring target is an engine forreusable spacecraft.